The research team trained the system on more than 33,000 dermoscopic images paired with clinical metadata.
Scientists have developed an artificial intelligence (AI) model that can detect melanoma more accurately by combining skin images with patient metadata. The study, published in Information Fusion, achieved 94.5% accuracy.
Melanoma remains one of the hardest skin cancers to diagnose because it often mimics harmless moles or lesions. While most AI tools rely on dermoscopic images alone, they often overlook patient information like age, gender, or where on the body the lesion appears, which can improve diagnostic accuracy.
To bridge that gap, Gwangill Jeon, PhD, from the department of embedded systems engineering at Incheon National University in South Korea, in collaboration with the University of West of England, Anglia Ruskin University, and the Royal Military College of Canada, created a deep learning model that integrates patient data and dermoscopic images.
“Skin cancer, particularly melanoma, is a disease in which early detection is critically important for determining survival rates,” says Jeon in a release. “Since melanoma is difficult to diagnose based solely on visual features, I recognized the need for AI convergence technologies that can consider both imaging data and patient information.”
Using the large-scale SIIM-ISIC melanoma dataset, which contains over 33,000 dermoscopic images paired with clinical metadata, the team trained their AI model to recognize subtle links between what appears on the skin and who the patient is. The model achieved 94.5% accuracy and an F1-score of 0.94, outperforming popular image-only models such as ResNet-50 and EfficientNet.
The researchers also performed feature importance analysis to make the system more transparent and robust. Factors like lesion size, patient age, and anatomical site were found to contribute strongly to accurate detection. These insights can help doctors understand and provide a roadmap to trust the diagnosis performed by AI.
“The model is not merely designed for academic purposes. It could be used as a practical tool that could transform real-world melanoma screening,” says Jeon in a release. “This research can be directly applied to developing an AI system that analyzes both skin lesion images and basic patient information to enable early detection of melanoma.”
In the future, the model could power smartphone-based skin diagnosis applications, telemedicine systems, or AI-assisted tools in dermatology clinics, helping reduce misdiagnosis rates and improve access to care, according to the researchers.
Photo caption: A new deep learning system developed by an international research team detects melanoma with 94.5% accuracy by fusing dermoscopic images and patient metadata such as age, gender, and lesion location. The approach enhances diagnostic precision, transparency, and access to early skin cancer detection through smart healthcare technology.
Photo credit: Incheon National University
